Resources

computational content analysis, computer vision

Tunstall, Von Werra, and Wolf (2022)

Lakshmanan, Görner, and Gillard (2021)

Vasilev (2019)

computational content analysis, natural language processing

Tunstall, Von Werra, and Wolf (2022)

Vasiliev (2020)

Szeliski (2010)

computational data analysis

McKinney (2022)

(some-test?)

(some-test?)

course literature

Neuendorf (2017)

Kedia and Rasu (2020)

Szeliski (2010)

websites and apps

• https://www.python.org/downloads/

• https://wiki.python.org/moin/BeginnersGuide

• https://www.anaconda.com/products/individual

• https://code.visualstudio.com/download

• https://github.com/jupyterlab/jupyterlab_app#download

• https://trinket.io/home

online articles

• some text here Kedia and Rasu (2020)

References

Kedia, Aman, and Mayank Rasu. 2020. Hands-on Python Natural Language Processing: Explore Tools and Techniques to Analyze and Process Text with a View to Building Real-World NLP Applications. Packt Publishing Ltd.

Lakshmanan, Valliappa, Martin Görner, and Ryan Gillard. 2021. Practical Machine Learning for Computer Vision. " O’Reilly Media, Inc.".

McKinney, Wes. 2022. Python for Data Analysis. " O’Reilly Media, Inc.".

Neuendorf, Kimberly A. 2017. The Content Analysis Guidebook. sage.

Szeliski, Richard. 2010. Computer Vision: Algorithms and Applications. Springer Science & Business Media.

Tunstall, Lewis, Leandro Von Werra, and Thomas Wolf. 2022. Natural Language Processing with Transformers. " O’Reilly Media, Inc.".

Vasilev, Ivan. 2019. Advanced Deep Learning with Python: Design and Implement Advanced Next-Generation AI Solutions Using TensorFlow and PyTorch. Packt Publishing Ltd.

Vasiliev, Yuli. 2020. Natural Language Processing with Python and spaCy: A Practical Introduction. No Starch Press.